For the last two years, I have worked with teaching Computer Science for young people.

This venture has had its ups and downs.

But we have had the support of many who believed in the vision.

So, It was very nice to see this

We (Countdown Institute – i.e. now me and Richard Schuchts based in Miami ) submitted an entry in the ASE AstroSat Challenge (supported by Northrop Grumman Corporation). The Association of Space Explorers is the unique professional organization composed of astronauts who have orbited Earth. They have 375 members from 35 countries and are passionate about encouraging students to pursue science, technology, engineering, and math education, as well as careers in astronautics. The ASE AstroSat Challenge is designed to give students a taste of the exciting world of satellite operations. The ASE AstroSat Challenge is made possible with the generous support of the Northrop Grumman Corporation.

Only 15 teams were selected to run a Space experiment – And our team (Miami Young Data Scientists/Countdown Institute) were one of them

Its amazing to get here.

It means the team of ‘young data scientists’ from Miami will be able to run a Space Experiment live in Space and also learn Data Science

The winning entry was based on teaching Data Science to young people.

Specifically, using Regression algorithms to make predictions on Space data from Ardusat (more on this soon)

This is different from our original idea and is more complex .. but I think it would make a difference to get more young people into Data Science (as per Harvard – the hottest profession in future)

Thus, I think the biggest winners are the young people of Miami who are a part of the winning team.

The main variation/evolution from the original idea is to focus on Data Science and inspiring students to take up Data Science through visualization of data and predictions using scientific methodology.

Its a way to get more students(both boys and girls) interested in Data science using Space exploration by coding on a live satellite.

Hence, the regression algorithms/iPython notebooks etc.

Also a bit more math. and hence slightly for older students(aroundn 15 to 17). All this also aligns with my ‘day job’ so to speak!

Note that the modules are customizable i.e. as per your personal learning plan – you may choose to do more or less of a specific topic. For example, more Deep Learning vs Sensor fusion. But overall, we will follow this plan.

Last week, I was approached by a program at Stanford University about this work

In a nutshell, the content of the Data Science for IoT course will be included as a recommended book for a forthcoming program taught at Stanford University

Its been a while since I have written a book ..

Excluding books for young people(teaching coding and computer science), the last major effort was with Tony Fish (Mobile Web 2.0) which launched my career into Mobile.

A book is a major undertaking

However, the existing course (Data Science for IoT) and the collaboration with Stanford University program for the book gives me the opportunity to create the book iteratively (in sections as we teach)

The book will have co-authors (more on that soon) and also many contributors from the Data Science for IoT course

This enables me to keep the content very fresh – which is critical in such a rapidly evolving field

We have had a great response to the Data Science for IoT course from all over the world.

Most of the participants are from USA and UK – but we also have participants from as far as Australia and Nicaragua.